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Small Molecule Azaacene as an Anode Material for Lithium-Ion Batteries

2023· article· en· W4386119038 on OpenAlexafffund
James Sturman, Eloi Grignon, Bryony T. McAllister, Chae-Ho Yim, Elena A. Baranova, Dwight S. Seferos, Yaser Abu‐Lebdeh

Bibliographic record

VenueEnergy & Fuels · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of TorontoNational Research Council CanadaUniversity of Ottawa
FundersOffice of Energy Research and DevelopmentNatural Sciences and Engineering Research Council of Canada
KeywordsAnodeElectrochemistryMaterials scienceCyclic voltammetryLithium (medication)Amorphous solidElectrodeIonDiffusionNanotechnologyMoleculeChemical engineeringChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In recent years, organic electrodes have attracted much attention owing to their unique properties including low density, flexibility, and in certain cases ultra-high capacity. In this contribution, we report the synthesis of a novel small molecule azaacene (SmAz) and reveal its electrochemical performance as an anode material in Li-ion batteries. SmAz delivers a reversible capacity of around 550 mA h g –1 at 50 mA g –1, a value that exceeds its theoretical capacity (237 mA h g –1 ). Moreover, SmAz displays an activation process in which the capacity increases with cycle time. Operando XRD suggests SmAz becomes amorphous during cycling. Greater insight into the charge storage mechanism is revealed with impedance and cyclic voltammetry and suggests the organic anode is controlled by a mixture of ion-diffusion and pseudocapacitive processes. Overall, these results reveal a new mechanistic understanding of the electrochemical behaviour in organic electrodes, as well as design features for their continued development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.245
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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